Read count benchmark at species level.

<p>Read count benchmark at species level.</p

Mock bacterial composition of <i>in vitro</i> and <i>in silico</i> datasets.

<p>Mock bacterial composition of <i>in vitro</i> and <i>in silico</i> datasets.</p

Read count benchmark at genus level.

<p>Read count benchmark at genus level.</p

Benchmarking of the <i>in vitro</i> data mapped against a bacteria reference sequence database.

<p>Benchmarking of the <i>in vitro</i> data mapped against a bacteria reference sequence database.</p

Benchmarking of the <i>in silico</i> data mapped against a bacteria reference sequence database.

<p>Benchmarking of the <i>in silico</i> data mapped against a bacteria reference sequence database.</p

A schematic flowchart for processing of paired-end sequences with MGmapper.

<p>MGmapper processes fastq reads in four steps. These consist of: (I) Trimming and mapping reads against a phiX bacteriophage to remove potential positive control reads. (II) Mapping to specified reference databases, post-processing of BWA-mem alignments to remove reads with low alignment score or insufficient alignment coverage. (III) Identification of best hits in <i>bestmode</i>: Assignment of a read-pairs to only one specific reference sequence based on the highest sum of alignment scores. In <i>fullmode</i>, assigned a read-pair to a reference sequence even if a higher alignment score is found when mapping to another reference sequence database. This will provide best target match, considering only the sequences present in one particular reference database. (IV) Compilation of abundance statistics, read and nucleotide counts, depth, coverage, and summary reports.</p

KmerFinder top results for sample <i>S</i>. <i>aureus</i> F38.

<p>The result shows that there are more than just significant hits for <i>S</i>. <i>aureus</i>. This indicates that the sample was contaminated and thus not a single isolate.</p

Flowchart depicting the workflow of the BAP.

<p>The input from the user is assembled if needed, and the bacterial species is identified through the KmerFinder algorithm. When ready, the assembled contigs are submitted to the ContigAnalyzer and ResFinder for annotation of contig metrics and identification of resistance genes. If the bacterial species is identified, the contigs are further, if applicable, submitted to MLST, PlasmidFinder and VirulenceFinder to identify the sequence type, known plasmids (and, if applicable, their plasmid sequence type), and known virulence genes. When all services are done, the BAP produces a summary report of the services result (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157718#pone.0157718.g002" target="_blank">Fig 2</a>).</p

Sequence type prediction by the MLST algorithm.

<p>Sequence type prediction by the MLST algorithm.</p

Species prediction by KmerFinder.

<p>Species prediction by KmerFinder.</p